The variational form of the reference encoder [1]} instead passes the GRU's output through two separate linear layers, one that predicts gaussian means and the other predicting gaussian variances, which are then reparameterised to a 32-dimensional latent z [2]} which is then passed to a final output layer generating a 32-dimension reference encoding. The latent z is trained to match a gaussian distributed variable by using a KL divergence loss term in the overall loss function. The weight given to this loss term during training is 0.001.
